Engineering data management for metal forming process

Finite element analysis (FEA) is a powerful tool to evaluate the formability of stamping parts during process and die design development procedures. However, it is very difficult to perform an exhaustive process design definition when many parameters play a fundamental role to define such a complex problem. So, under the needs of reduction in: design time, development cost and parts weight, there is an urgent need to develop and apply more efficient methods in order to improve the current design procedures. Currently, modeling and simulation are often integrated parts of product and process design development in a manufacturing environment, from this point of view, although design costs are usually only between 5 and 15% of the total production costs, the decisions made in this early stage significantly determine the overall manufacturing costs.

For a generic component it is clear how its shape, among several parameters, has a direct influence on its feasibility. Starting from this assumption, the authors have developed a new approach grouping components upon their shapes analyzing component formability within a given “component family”. Nowadays, it exists only a process designer “sensitivity” that produces a ranking upon shape/feasibility ratio. Having as reference industrial test cases, the authors have defined appropriate shape parameters in order to have dimensionless coefficients representative for the given geometries. These shape parameters are used to track the process performances through their variation thanks to the usage of the numerical simulation. FEA has been extensively used in order to: define, investigate and validate each shape parameter with a proper comparison to the macro feasibility of the chosen component geometry. The feasibility configuration definition, for a given shape, has been made through an appropriate study of the influence of each process variable on the properly process performances. In order to define an objective evaluation of the process performance, appropriate Key Performance Indexes (KPI) have been developed.

In order to achieve the aim of a complete overview of the generated data space, the authors have used an innovative approach based upon the OLAP (On-Line Analytical Processing) database and a properly defined Engineering Intelligence model.

As the results of simulations are imported in OLAP database, it is very simple to navigate throughout the data space and to create clear graphs able to describe the effects of shape parameters on the monitored KPI. Once simulations’ results are imported in OLAP database, it is very simple to navigate throughout the data space and to create clear graphs able to describe the effects of shape parameters on the monitored KPI.

The Author

Teresa Primo & Barbara Manisi
assegnista di ricerca
UniversitĂ  del Salento
via per Monteroni
Corpo "O"
Lecce,
Italy